Posts

The leader of Brown’s NSF-funded AI research institute talks AI doom scenarios, the frontier labs’ “slowdown”, and making AI safer.

The course tackles cybersecurity issues including deepfakes, copyright, privacy, national security and decision-making.

A new project (“Where are the data workers behind AI?”) from the Center for Technological Responsibility, Reimagination and Redesign is an investigative attempt to make the data annotation and labeling industry transparent.

 

 

In a new blog post, Emily Hong, who co-led the CNTR AISLE Legislation Lab with Dre Boyd-Weatherly, takes a moment to reflect on the lab’s journey of success, growth, and iteration thus far.

A recent Brown+Beyond event welcomed alums and friends to a discussion with two campus AI leaders, Michael Littman and Brenda Rubenstein.

The need is clear: AI is rapidly permeating journalism, increasing dependency on a few Big Tech companies who control much of the infrastructure underpinning commercial AI. Many in the industry have raised alarms about what this means for editorial independence, labor, and the long-term viability of journalism itself.

The inaugural conference AI & Gender-Based Harms: Implications for Policy and Practice examined the impact of AI-facilitated gender-based harms and response policies with an interdisciplinary roster of researchers, clinicians, advocates, policymakers, and practitioners. Brown CS and Data Science Institute faculty member Diana Freed co-founded and co-chaired the conference with colleagues from Fordham University, the NYC Mayor's Office to End Domestic and Gender-Based Violence, and Weill Cornell Medicine.

With a course offered this past spring semester, professors and students alike have begun grappling with the role automated AI agents have in teaching students the basics of software development.

"Most benchmarks for AI agents," the authors explain, "usually only ask one question, did the agent complete the task? And what they don't ask is whether the agent should have completed the task in the first place."

She'll study how people communicate goals to machines and design AI systems that can interpret imperfect instructions by reasoning about the intent behind them. Expected outcomes include safer decision-making technologies and new tools that help organizations deploy AI more effectively.